Scale Invariant Feature Transform Research Articles

Matching UAV-Based Simulated High-Resolution SAR and Real SAR Images

Abstract. Our experiment focuses on generating simulated high-resolution (HR) Synthetic Aperture Radar (SAR) images from the photogrammetric 3D model derived from Unmanned Aerial Vehicle (UAV) imagery. The goal is to match these simulated images with real SAR data of complex scenes containing vegetation, buildings, roads, and the construction of receiving antennas. Simulated SAR reflectivity map was based on ray tracing techniques.Two feature matching algorithms, namely Scale-Invariant Feature Transform (SIFT) and Oriented FAST and Rotated BRIEF (ORB) were employed for co-registration of the simulated to the real SAR images. The effectiveness of these algorithms is evaluated by their matching rates. The experiment results of the matching process highlight the superior performance of ORB over SIFT, attributed to its robustness against rotation variations and noise. ORB emerges as a versatile choice for high-resolution real and simulated SAR registration tasks.

An Efficient Search on Cloud Images with Blockchain Technology

An Efficient Search on Cloud Images with Blockchain Technology

Radiographic chest wall abnormalities in primary spontaneous pneumothorax identified by artificial intelligence

Primary spontaneous pneumothorax (PSP) primarily affects slim and tall young males. Exploring the etiological link between chest wall structural characteristics and PSP is crucial for advancing treatment methods. In this case-control study, chest computed tomography (CT) images from patients undergoing thoracic surgery, with or without PSP, were analyzed using Artificial Intelligence. Convolutional Neural Network (CNN) model of EfficientNetB3 and InceptionV3 were used with transfer learning on the Imagenet to compare the images of both groups. A heatmap was created on the chest CT scans to enhance interoperability, and the scale-invariant feature transform (SIFT) was adopted to further compare the image level. A total of 2,312 CT images of 26 non-PSP patients and 1,122 CT images of 26 PSP patients were selected. Chest-wall apex pit (CAP) was found in 25 PSP and three non-PSP patients (p < 0.001). The CNN achieved a testing accuracy of 93.47 % in distinguishing PSP from non-PSP based on chest wall features by identifying the existence of CAP. Heatmap analysis demonstrated CNN's precision in targeting the upper chest wall, accurately identifying CAP without undue influence from similar structures, or inappropriately expanding or minimizing the test area. SIFT results indicated a 10.55 % higher mean similarity within the groups compared to between PSP and non-PSP (p < 0.001). In conclusion, distinctive radiographic chest wall configurations were observed in PSP patients, with CAP potentially serving as an etiological factor linked to PSP. This study accentuates the potential of AI-assisted analysis in refining diagnostic approaches and treatment strategies for PSP.

Handwritten Signature Verification System Using User-dependent Approach: A Comparative Study

Handwritten signature verification is a critical aspect of personal authentication in numerous sectors, including banking, legal, and access control systems. This paper provides a comprehensive investigation into writer-dependent signature verification techniques, spanning from traditional approaches such as Histogram of Oriented Gradients (HOG) and Scale-Invariant Feature Transform (SIFT) to cutting-edge deep learning architectures like Siamese and triplet networks. Through extensive experimentation and meticulous analysis conducted on benchmark datasets, we rigorously compare and evaluate the efficacy of these approaches. Our study reveals intriguing insights into the performance characteristics of different methodologies, shedding light on their strengths, weaknesses, and areas for improvement. Notably, we observe that while traditional feature-based methods excel in capturing discriminative information from signature images, deep learning architectures offer enhanced flexibility and adaptability, particularly in scenarios with large and diverse datasets. By combining the strengths of both paradigms, we demonstrate the potential for building robust and reliable signature verification systems capable of handling real-world challenges and variations. We highlight the importance of carefully curated datasets and well-designed network architectures in achieving optimal results, thereby providing valuable guidelines for practitioners and researchers in the field. In conclusion, this study contributes significant insights to the advancement of handwritten signature verification systems, emphasizing the importance of leveraging both traditional methodologies and modern deep learning techniques for building secure and dependable authentication mechanisms. By addressing key challenges and exploring novel approaches, our research aims to foster innovation and enhance the security measures associated with handwritten signature authentication in diverse applications. Keywords—Handwritten signature verification, Writer-dependent approach, Feature-based methods, Deep learning, Siamese networks, Triplet networks, Benchmark datasets, Performance evaluation, Authentication mechanisms, Security measures.

A Large-Crack Image-Stitching Method with Cracks as the Regions of Interest

While crack detection is crucial for maintaining concrete structures, existing methods often overlook the analysis of large cracks that span multiple images. Such analyses typically rely on image stitching to create a complete image of a crack. Current stitching methods are not only computationally demanding but also require manual adjustments; thus, a fast and reliable solution is still lacking. To address these challenges, we introduce a stitching method that leverages the advantages of crack image-segmentation models. This method first utilizes the Mask R-CNN model for the identification of crack regions as regions of interest (ROIs) within images. These regions are then used to calculate keypoints of the scale-invariant feature transform (SIFT), and descriptors for these keypoints are computed with the original images for image matching and stitching. Compared with traditional methods, our approach significantly reduces the computational time; by 98.6% in comparison to the Brute Force (BF) matcher, and by 58.7% with respect to the Fast Library for Approximate Nearest Neighbors (FLANN) matcher. Our stitching results on images with different degrees of overlap or changes in shooting posture show superior structural similarity index (SSIM) values, demonstrating excellent detail-matching performance. Moreover, the ability to measure complete crack images is indicated by the relative error of 7%, which is significantly better than that of traditional methods.

ACCURACY ASSESSMENT OF THE EFFECT OF DIFFERENT FEATURE DESCRIPTORS ON THE AUTOMATIC CO-REGISTRATION OF OVERLAPPING IMAGES

This research seeks to assess the effect of different selected feature descriptors on the accuracy of an automatic image registration scheme. Three different feature descriptors were selected based on their peculiar characteristics, and implemented in the process of developing the image registration scheme. These feature descriptors (Modified Harris and Stephens corner detector (MHCD), the Scale Invariant Feature Transform (SIFT) and the Speeded Up Robust Feature (SURF)) were used to automatically extract the conjugate points common to the overlapping image pairs used for the registration. Random Sampling Consensus (RANSAC) algorithm was used to exclude outliers and to fit the matched correspondences, Sum of Absolute Differences (SAD) which is a correlation-based feature matching metric was used for the feature match, while projective transformation function was used for the computation of the transformation matrix (T). The obtained overall result proved that the SURF algorithm outperforms the other two feature descriptors with an accuracy measure of -0.0009 pixels, while SIFT with a cumulative signed distance of 0.0328 pixels also proved to be more accurate than MHCD with a cumulative signed distance of 0.0457 pixels. The findings affirmed the importance of choosing the right feature descriptor in the overall accuracy of an automatic image registration scheme.

Cone Beam Computerized Tomography Preoperative Guidance under Feature Point Extraction Algorithm Combined with Autologous Concentrated Growth Factors in Maxillary Dental Implantation with Insufficient Bone Mass

ABSTRACT Objective It was to assess the efficacy of preoperative guidance using cone beam computed tomography (CBCT) and the application of autologous concentrated growth factors (CGF) for implantation in maxillary teeth with inadequate bone mass. Methods An eigenvector-based multimodal elasticity algorithm was developed. Eighty patients with insufficient bone mass in the maxillary dental region were rolled into control group (n = 40, Bio-Oss bone powder treatment) and observation group (n = 40, autologous CGF and Bio-Oss bone powder treatment). The scale-invariant feature transform (SIFT) algorithm and the hierarchical attribute matching mechanism for elastic registration (HAMMER) were introduced, whose registration time and mutual information value were compared. Therapeutic outcomes of patients were also compared. Results The eigenvector-based multimodal elastic algorithm exhibited a significantly shorter registration time versus SIFT and HAMMER algorithms, and superior accuracy, sensitivity, and specificity (p < .05). Observation group demonstrated greater alveolar ridge elevation and bone augmentation versus control group, implying superior healing outcomes. Conclusion The feature point extraction algorithm is applicable for CBCT image processing in patients with insufficient maxillary bone mass. Autologous CGF exhibited superior therapeutic efficacy.

Detection and Imaging of Corrosion Defects in Steel Structures Based on Ultrasonic Digital Image Processing

Corrosion is one of the critical factors leading to the failure of steel structures. Ultrasonic C-scans are widely used to identify corrosion damage. Limited by the range of C-scans, multiple C-scans are usually required to cover the whole component. Thus, stitching multiple C-scans into a panoramic image of the area under detection is necessary for interpreting non-destructive testing (NDT) data. In this paper, an image mosaic method for ultrasonic C-scan based on scale invariant feature transform (SIFT) is proposed. Firstly, to improve the success rate of registration, the difference in the probe starting position in two scans is used to filter the matching pairs of feature points obtained by SIFT. Secondly, dynamic programming methods are used to search for the optimal seam path. Finally, the pixels in the overlapping area are fused by fade-in and fade-out fusion along the seam line. The improved method has a higher success rate of registration and lower image distortion than the conventional method in the mosaic of ultrasonic C-scan images. Experimental results show that the proposed method can stitch multiple C-scan images of a testing block containing artificial defects into a panorama image effectively.

Surround Sensing Technique for Trucks Based on Multi-Features and Improved Yolov5 Algorithm.

The traditional rearview mirror method cannot fully guarantee safety when driving trucks. RGB and infrared images collected by cameras are used for registration and recognition, so as to achieve the perception of surroundings and ensure safe driving. The traditional scale-invariant feature transform (SIFT) algorithm has a mismatching rate, and the YOLO algorithm has an optimization space in feature extraction. To address these issues, this paper proposes a truck surround sensing technique based on multi-features and an improved YOLOv5 algorithm. Firstly, the edge corner points and infrared features of the preset target region are extracted, and then a feature point set containing the improved SIFT algorithm is generated for registration. Finally, the YOLOv5 algorithm is improved by fusing infrared features and introducing a composite prediction mechanism at the prediction end. The simulation results show that, on average, the image stitching accuracy is improved by 17%, the time is reduced by 89%, and the target recognition accuracy is improved by 2.86%. The experimental results show that this method can effectively perceive the surroundings of trucks, accurately identify targets, and reduce the missed alarm rate and false alarm rate.

Computational Analysis of Single Nucleotide Polymorphisms in Human HIC1 Gene.

Background A putative tumor suppressor gene called HIC1 (hypermethylated in cancer) is situated at 17p13.3, a locus where the allelic loss occurs often in human malignancies, including breast cancer. Hypermethylated in cancer 1 protein is a protein that in humans is encoded by the HIC1 gene and it's aHomo sapiens(Human). This gene functions as a growth regulatory and tumor repressor gene. The molecular function of HIC1 gene includes DNA-binding transcription factor activity, sequence-specific DNA binding, DNA binding, histone deacetylase binding, protein binding, metal ion binding, nucleic acid binding, DNA-binding transcription repressor activity, RNA polymerase II-specific, DNA-binding transcription factor activity, RNA polymerase II-specific. The biological process of HIC1 gene includes multicellular organism development, negative regulation of Wnt signaling pathway, positive regulation of DNA damage response, signal transduction by p53 class mediator regulation of transcription, DNA-templated, negative regulation of transcription by RNA polymerase II, Wnt signaling pathway, transcription, DNA-templated, intrinsic apoptotic signaling pathway in response to DNA damage, cellular response to DNA damage stimulus. The study aimed to predict the stability and structure of the protein that will arise from single nucleotide polymorphisms (SNPs) in the human HIC1 gene. Methodology To investigate the possible negative effects associated with these SNPs, bioinformatic analysis is typically essential. The following tools were employed for forecasting harmful SNPs: scale-invariant feature transform (SIFT), Protein Analysis Through Evolutionary Relationships (PANTHER), nonsynonymous SNP by Protein Variation Effect Analyzer (PROVEAN), and nonsynonymous SNP by Single Nucleotide Polymorphism Annotation Platform (SNAP). Results The present study identified a total of 36 SNPs using the SIFT approach, which were shown to have functional significance. Twenty-six were determined to be tolerable, whereas 10 were shown to be detrimental. Out of 20 SNPs, seven (P370A, P646S, R654P, A476T, S400S, D666N, D7V) SNPs were predicted as "Possibly damaging" and seven (L9F, G468R, G490R, L482R, S12W, G489D, S12P) were identified as "probably benign", and six (R725G, G620S, A56V, E463D, D394N, L338V) were identified as "probably damaging" according to the predictions made by PANTHER tools. The majority of the pixels on the strip were red, indicating that the gene changes may have dangerous consequences. These results highlight the need for more research to fully comprehend how these mutations affect the hic1 protein's function, which is essential for the emergence of different types of cancer. Conclusion The current research has provided us with essential information about how SNPs might be used as a diagnostic marker for cancer, given that SNPs may be candidates for cellular changes caused by mutations linked to cancer.

Combining bag of visual words-based features with CNN in image classification

Abstract Although traditional image classification techniques are often used in authentic ways, they have several drawbacks, such as unsatisfactory results, poor classification accuracy, and a lack of flexibility. In this study, we introduce a combination of convolutional neural network (CNN) and support vector machine (SVM), along with a modified bag of visual words (BoVW)-based image classification model. BoVW uses scale-invariant feature transform (SIFT) and Oriented Fast and Rotated BRIEF (ORB) descriptors; as a consequence, the SIFT–ORB–BoVW model developed contains highly discriminating features, which enhance the performance of the classifier. To identify appropriate images and overcome challenges, we have also explored the possibility of utilizing a fuzzy Bag of Visual Words (BoVW) approach. This study also discusses using CNNs/SVM to improve the proposed feature extractor’s ability to learn more relevant visual vocabulary from the image. The proposed technique was compared with classic BoVW. The experimental results proved the significant enhancement of the proposed technique in terms of performance and accuracy over state-of-the-art models of BoVW.

Comprehensive Evaluation of Multispectral Image Registration Strategies in Heterogenous Agriculture Environment.

This article is focused on the comprehensive evaluation of alleyways to scale-invariant feature transform (SIFT) and random sample consensus (RANSAC) based multispectral (MS) image registration. In this paper, the idea is to extensively evaluate three such SIFT- and RANSAC-based registration approaches over a heterogenous mix containing Triticum aestivum crop and Raphanus raphanistrum weed. The first method is based on the application of a homography matrix, derived during the registration of MS images on spatial coordinates of individual annotations to achieve spatial realignment. The second method is based on the registration of binary masks derived from the ground truth of individual spectral channels. The third method is based on the registration of only the masked pixels of interest across the respective spectral channels. It was found that the MS image registration technique based on the registration of binary masks derived from the manually segmented images exhibited the highest accuracy, followed by the technique involving registration of masked pixels, and lastly, registration based on the spatial realignment of annotations. Among automatically segmented images, the technique based on the registration of automatically predicted mask instances exhibited higher accuracy than the technique based on the registration of masked pixels. In the ground truth images, the annotations performed through the near-infrared channel were found to have a higher accuracy, followed by green, blue, and red spectral channels. Among the automatically segmented images, the accuracy of the blue channel was observed to exhibit a higher accuracy, followed by the green, near-infrared, and red channels. At the individual instance level, the registration based on binary masks depicted the highest accuracy in the green channel, followed by the method based on the registration of masked pixels in the red channel, and lastly, the method based on the spatial realignment of annotations in the green channel. The instance detection of wild radish with YOLOv8l-seg was observed at a mAP@0.5 of 92.11% and a segmentation accuracy of 98% towards segmenting its binary mask instances.

Research on image deformation monitoring algorithm based on binocular vision

Monitoring the deformation of critical structures such as tunnel、buildings and bridges is critical to ensure the safety of infrastructures. Non-contact deformation monitoring technology based on digital imagery has emerged as a prevalent and effective monitoring method, which enables cost-effective and efficient tracking of structural deformations. This paper aims to develop an automated deformation monitoring methodology using the binocular vision. First, standard preprocessing of digital images is performed through the integration of filtering algorithms and image enhancement techniques. Furthermore, by combining the Scale Invariant Feature Transform (SIFT) feature matching algorithm, the inverse compositional gauss–newton iterative algorithm, and interpolation methodologies, the planar displacement of the monitored targets can be calculated precisely, with sub-pixel precision being achieved. Subsequently, by adopting an enhanced Semi-Global Matching (SGM) algorithm, stereo matching of binocular images were accomplished. This process yields a disparity map, from which the depth map is computed. Then, precise measurement of the vertical displacement of the monitoring target can be obtained. This enables accurate measurement of three-dimensional displacements of the monitored targets. Finally, the developed methodology undergoes a comparative assessment against traditional measurement methods and existing algorithms to validate its measurement accuracy. The results prove that the deformation measurement technique based on binocular vision proposed in this paper captures structural deformation effectively. The accuracy surpasses that of existing measurement algorithms, and its efficiency outperforms traditional measurement methods.

An FPGA‐based tool for supporting the design, modeling, and evaluation of hybrid object recognition systems on computer engineering courses

AbstractThe field of computer vision is characterized by computationally intensive algorithms and techniques with strict real‐time requirements. Field programmable gate arrays (FPGAs) are based on a concurrent paradigm which allows the design of efficient hardware architectures and has positioned FPGAs as an ideal device for implementing compute‐intensive applications. For this reason, FPGA technology has had a great impact in areas such as computer vision, where one of the main objectives for researchers working in this field is to create efficient automatic object recognition systems. Therefore, the need to provide undergraduates with the necessary skills to design FPGA‐based object recognition systems is evident. With this aim in mind, it is essential that specialization courses related to the design of these systems include the required resources for the student to apply the theoretical knowledge in solving practical problems. In this article, we present a development tool designed to help students, teachers, and researchers during the design‐modeling‐implementation process of object recognition systems based on FPGAs. The proposed tool operates under a modular approach as this facilitates the working on any of the phases of a recognition system and it is considered as a hybrid because the other phases can be developed using a software language. An empirical evaluation involving undergraduates enrolled in a Computer Engineering program was conducted to create a hardware architecture for the DAISY descriptor that uses the homogeneous features of objects immersed in images to produce an efficient representation. By considering similar descriptors such as Scale‐Invariant Feature Transform (SIFT) and Histogram of Oriented Gradients (HOG), DAISY is computed by convolving orientation maps instead of using weighted sums of gradient norms, which offers the same kind of invariance at a lower computational cost for the dense case. The results obtained during such an evaluation indicated that students consider this FPGA‐based tool to be an alternative to receiving practical training on designing systems for solving problems related to the area of object recognition.

Scalar invariant transform based deep learning framework for detecting heart failures using ECG signals

Heart diseases are leading to death across the globe. Exact detection and treatment for heart disease in its early stages could potentially save lives. Electrocardiogram (ECG) is one of the tests that take measures of heartbeat fluctuations. The deviation in the signals from the normal sinus rhythm and different variations can help detect various heart conditions. This paper presents a novel approach to cardiac disease detection using an automated Convolutional Neural Network (CNN) system. Leveraging the Scale-Invariant Feature Transform (SIFT) for unique ECG signal image feature extraction, our model classifies signals into three categories: Arrhythmia (ARR), Congestive Heart Failure (CHF), and Normal Sinus Rhythm (NSR). The proposed model has been evaluated using 96 Arrhythmia, 30 CHF, and 36 NSR ECG signals, resulting in a total of 162 images for classification. Our proposed model achieved 99.78% accuracy and an F1 score of 99.78%, which is among one of the highest in the models which were recorded to date with this dataset. Along with the SIFT, we also used HOG and SURF techniques individually and applied the CNN model which achieved 99.45% and 78% accuracy respectively which proved that the SIFT–CNN model is a well-trained and performed model. Notably, our approach introduces significant novelty by combining SIFT with a custom CNN model, enhancing classification accuracy and offering a fresh perspective on cardiac arrhythmia detection. This SIFT–CNN model performed exceptionally well and better than all existing models which are used to classify heart diseases.

Individual recognition of Eurasian beavers (Castor fiber) by their tail patterns using a computer-assisted pattern-identification algorithm.

Individual recognition of animals is an important aspect of ecological sciences. Photograph-based individual recognition options are of particular importance since these represent a non-invasive method to distinguish and identify individual animals. Recent developments and improvements in computer-based approaches make possible a faster semi-automated evaluation of large image databases than was previously possible. We tested the Scale Invariant Feature Transform (SIFT) algorithm, which extracts distinctive invariant features of images robust to illumination, rotation or scaling of images. We applied this algorithm to a dataset of 800 tail pattern images from 100 individual Eurasian beavers (Castor fiber) collected as part of the Norwegian Beaver Project (NBP). Images were taken using a single-lens reflex camera and the pattern of scales on the tail, similar to a human fingerprint, was extracted using freely accessible image processing programs. The focus for individual recognition was not on the shape or the scarring of the tail, but purely on the individual scale pattern on the upper (dorsal) surface of the tail. The images were taken from two different heights above ground, and the largest possible area of the tail was extracted. The available data set was split in a ratio of 80% for training and 20% for testing. Overall, our study achieved an accuracy of 95.7%. We show that it is possible to distinguish individual beavers from their tail scale pattern images using the SIFT algorithm.

Computational Exploration of Single-Nucleotide Polymorphisms in the Human hRAS Gene: Implications and Insights.

Background A group of genes called oncogenes includes the Harvey rat sarcoma virus (hRAS) gene. Along with hRAS, Kirsten rat sarcoma viral oncogene homolog (kRAS) and neuroblastoma RAS viral oncogene homolog(nRAS) genes belong to the Rat sarcoma (Ras) family of oncogenes. These three genes result in Rho guanosine triphosphate hydrolases (GTPases) as their protein product. Instructions for producing the protein hRAS, which is mainly involved in controlling cell division, are provided by the hRAS gene. The hRAS protein transfers signals from outside through a process called signal transduction.Because the hRAS protein is a GTPase, it changes the chemical guanosine-5'-triphosphate (GTP) into guanosine diphosphate (GDP). GTP and GDP molecules operate as switches to turn on and off the hRAS. This study aimed to anticipate the structure and stability of the protein resulting from missense single-nucleotide polymorphisms (SNPs) in the human hRAS genes. Methodology To investigate the possible negative effects associated with these SNPs, bioinformatic analysis is typically essential. The following tools were employed for forecasting harmful SNPs: Scale-Invariant Feature Transform (SIFT), Protein Analysis Through Evolutionary Relationships (PANTHER), non-synonymous SNP by Protein Variation Effect Analyzer (PROVEAN), and non-synonymous SNP by Single Nucleotide Polymorphism Annotation Platform (SNAP). Results The present study identified a total of 11 SNPs using the SIFT approach, which were shown to have functional significance. Only two of these 11 SNPs were determined to be tolerable, whereas nine were shown to be detrimental. Among the 11 SNPs analyzed, seven (Q61H, Q99H, K117R, A121D, A146V, R169W, R169Q) were classified as possibly damaging,and four (G13V, Q22K, Q61K, Q13V) were categorized as probably benign according to the predictions made by PANTHER tools. Therefore, the seven SNPs were identified as high-risk SNPs. Conclusions Given that SNPs have the potential to be candidates for cellular alterations brought on by mutations that are associated with cancer, this study provides vital information about how SNPs might be utilized as a diagnostic marker for cancer.

Advanced framework for epilepsy detection through image-based EEG signal analysis.

Recurrent and unpredictable seizures characterize epilepsy, a neurological disorder affecting millions worldwide. Epilepsy diagnosis is crucial for timely treatment and better outcomes. Electroencephalography (EEG) time-series data analysis is essential for epilepsy diagnosis and surveillance. Complex signal processing methods used in traditional EEG analysis are computationally demanding and difficult to generalize across patients. Researchers are using machine learning to improve epilepsy detection, particularly visual feature extraction from EEG time-series data. This study examines the application of a Gramian Angular Summation Field (GASF) approach for the analysis of EEG signals. Additionally, it explores the utilization of image features, specifically the Scale-Invariant Feature Transform (SIFT) and Oriented FAST and Rotated BRIEF (ORB) techniques, for the purpose of epilepsy detection in EEG data. The proposed methodology encompasses the transformation of EEG signals into images based on GASF, followed by the extraction of features utilizing SIFT and ORB techniques, and ultimately, the selection of relevant features. A state-of-the-art machine learning classifier is employed to classify GASF images into two categories: normal EEG patterns and focal EEG patterns. Bern-Barcelona EEG recordings were used to test the proposed method. This method classifies EEG signals with 96% accuracy using SIFT features and 94% using ORB features. The Random Forest (RF) classifier surpasses state-of-the-art approaches in precision, recall, F1-score, specificity, and Area Under Curve (AUC). The Receiver Operating Characteristic (ROC) curve shows that Random Forest outperforms Support Vector Machine (SVM) and k-Nearest Neighbors (k-NN) classifiers. The suggested method has many advantages over time-series EEG data analysis and machine learning classifiers used in epilepsy detection studies. A novel image-based preprocessing pipeline using GASF for robust image synthesis and SIFT and ORB for feature extraction is presented here. The study found that the suggested method can accurately discriminate between normal and focal EEG signals, improving patient outcomes through early and accurate epilepsy diagnosis.

Penerapan Metode Eigenface Untuk Pencocokan Wajah Dengan Menggunakan Scale Invariant Feature Transform

The problem in this study is matching face objects to recognize a person's identity that can be used in the field of data security, so to secure a data is encoded by using a facial image so that data access is maintained from parties who do not need to access it. One solution to the problems described above is to apply a method that can recognize the sutu object. One method that is often used is Invariant Feature Transform (SIFT). Scale Invariant Feature Transform (SIFT) is a method for detecting and describing local features in an image. The author did not find the same Title of the previous study, but the author found the previous research using the same method among others. From the results obtained in table 4.1, this method is able to identify faces by comparing keypoint values, where as in step 7 the keypoint obtained against the previous sample image only has 2 keypoints produced, where the sample face image has 64 keypoints while the results obtained on the test image have 66 keypoints.

Towards a true conservation zero tillage system: “A proposed solution based on computer vision to herbicide resistance.”

The Zero Tillage (ZT) or Non-Tillage system constitutes an agricultural production approach designed for improved soil conservation, reduced fossil fuel usage, mitigation of waterway pollution, water and wind erosion, and addressing soil compaction, among other objectives. In this way, ZT promises more sustainable agriculture. However, current ZT production systems depend on herbicide applications for weed control. The use of herbicides with the same modes of action over many years has led to numerous resistant weed species, which end up threatening the success of the herbicide weed control and, consequently, the overall success of the ZT system. Being able to automatically detect and classify weed species directly under the sprayer during the chemical application is an alternative to control populations of weeds since it would allow selecting the herbicide and dose for those particular species. This study, conducted over 15 years in commercial fields utilizing Zero Tillage (ZT) management, examined various ground covers intrinsic to the ZT system originating from previous crops or residues post-harvest. ZT features a broad spectrum of contexts due to the complexity of different ground cover types, which challenge computer vision techniques. The assessment focused on the automatic detection and classification of among the most problematic monocotyledonous and dicotyledonous weed species in the province of Cordoba in Argentina, with the objective of enabling a more targeted and selective approach to their control. These are Amaranthus palmeri, Echinochloa crus-galli, Eleusine indica, Parietaria debilis and Conyza sumatrensis. Additionally, some species belong to the same botanical families with several morphological resemblances that defy vision algorithms. This work proceeded through a machine learning approach applied to computer vision features such as SIFT (scale-invariant feature transform), K-means (clustering), and Bag of Features over the Support Vector Machine for classification. The results showed an accuracy between 89 % and 98 % over the species, allowing a significant reduction of herbicide while applying the adequate active ingredient. Moreover, virtual binary maps from weed patches are devised to be implemented through ISOBUS protocol. Thus the current research contributes significantly to the issue of controlling populations of weeds in the ZT agriculture system.